A Comparative Analysis of the Predictive Abilities of Economic Complexity Metrics Using International Trade Network

Abstract

The complex networks approach has proven to be an effective tool to understand and predict the evolution of a wide range of complex systems. In this work, we consider the network representing the exchange of goods between countries: the international trade network. According to the type of goods they export, the complex networks approach allows inferring which countries will have a bigger growth compared to others. The aim of this work is to study three different methods characterizing the complex networks and study their behaviour on two main topics. Can the method predict the economic evolution of a country? What happens to those methods when we merge the economies?

The Middle-Income Trap and the Coping Strategies From Network-Based Perspectives

“…Table 1 lists the recommended countries to the targeted ones for the “BRICS” countries plus Philippines, since Philippines has a promising development according to the fitness of countries in Refs. [ 3 , 27 ]. The recommendation list by seems more reasonable in practice.…”

“…We used the international trade data from 1962 to 2000 [ 16 , 27 , 33 ]. The country–product relationship was represented by a bipartite network in which one kind of nodes indicate the countries and the other ones indicate the products.…”

“…Please see Ref. [ 27 ] for the full procedure to process the original data. After filtering the data, the network contained 72 countries and 770 product categories.…”

Quantifying the Robustness of Countries’ Competitiveness by Network‐Based Methods

“…When we apply (3) to the country-product bipartite networks, developed countries and high-quality products are inclined to have high fitness, while developing countries and lowquality products tend to have small complexity. Therefore, FCM can evaluate the competitiveness of countries and the complexity of products [1,25] and predict the future economic development [19,27]. This method has been successfully applied to other fields, such as ecological networks [28] and scientific competitiveness of nations [29].…”

“…If a product is only produced by competitive countries, it is a high-quality product; otherwise it is a lowcomplexity product. FCM outperforms MR both in accuracy and efficiency [8,19]. The previous works mainly focused on how to accurately evaluate and predict the economic growth of countries.…”